Recently, deep learning (DL) based data-driven detectors such as Fully-Connected Neural Networks (FCN) [1]}, Recurrent Neural Network (RNN) [2]}, Convolutional Neural Network (CNN) [3]} have been proposed for cyberattack detection in power grids, thanks to the increasing volume of collected historical data samples.
However, despite their powerful modeling capabilities, DL approaches may not generalize the data well enough and fail to detect cyberattacks if their architecture ignores the underlying physical system generating the data [4]}.
For instance, RNNs are perfectly suited architectures to model the recurrent structure of the language data.
Similarly, CNNs are better candidates for image and video processing since sliding kernels are extremely efficient to exploit the pixel locality of image data [5]}.
